A robust neurocontroller incorporating a priori knowledge of plant dynamics

Abstract

This paper presents a nonlinear controller design method that integrates linear optimal control techniques and nonlinear neural networks. The multilayered neural networks (MNN's) are incorporated into a model-based linear optimal controller (LOR) to add nonlinear effects on the LOR. The proposed controller can tolerate a wider range of uncertainties than the LOR alone, because the MNN can compensate nonlinear system uncertainties that are not considered in the LOR design. The control performance is improved by using a priori knowledge of the plant dynamics as the system equation and the corresponding LOR. Using the similar technique, a nonlinear servo controller is designed by combining the MNN-based controller and the linear optimal servo controller. Computer simulations are performed to show the applicability and the limitation of the new nonlinear controllers.